Supported by Dr. Osamu Ogasawara and  providing  . |
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Last data update: 2014.03.03 |
Plot function for SimCi-objectsDescriptionA plot of the results of Usage## S3 method for class 'SimCi' plot(x, xlim, xlab, ylim, ...) Arguments
ValueA plot of the confidence intervals of a "SimCi" object. Author(s)Christof Kluss and Mario Hasler See Also
Examples
# Example 1:
# Simultaneous confidence intervals related to a comparison of the groups
# B and H against the standard S, on endpoint Thromb.count, assuming unequal
# variances for the groups. This is an extension of the well-known Dunnett-
# intervals to the case of heteroscedasticity.
data(coagulation)
interv1 <- SimCiDiff(data=coagulation, grp="Group", resp="Thromb.count",
type="Dunnett", base=3, alternative="greater", covar.equal=FALSE)
interv1
plot(interv1)
# Example 2:
# Simultaneous confidence intervals related to a comparisons of the groups
# B and H against the standard S, simultaneously on all endpoints, assuming
# unequal covariance matrices for the groups. This is an extension of the well-
# known Dunnett-intervals to the case of heteroscedasticity and multiple
# endpoints.
data(coagulation)
interv2 <- SimCiDiff(data=coagulation, grp="Group", resp=c("Thromb.count","ADP","TRAP"),
type="Dunnett", base=3, alternative="greater", covar.equal=FALSE)
summary(interv2)
par(mfrow=c(1,3)); plot(interv2)
# Example 3:
# Simultaneous confidence intervals for ratios of means, related to an all-pair
# comparison of the groups B, H and S, simultaneously on all endpoints, assuming unequal
# covariance matrices for the groups. This is an extension of the well-known Tukey-
# intervals to the case of heteroscedasticity and multiple endpoints, and in terms of
# ratios of means instead of differences.
data(coagulation)
interv3 <- SimCiRat(data=coagulation, grp="Group", resp=c("Thromb.count","ADP","TRAP"),
type="Tukey", alternative="two.sided", covar.equal=FALSE)
summary(interv3)
par(mfrow=c(3,1)); plot(interv3)
Results
R version 3.3.1 (2016-06-21) -- "Bug in Your Hair"
Copyright (C) 2016 The R Foundation for Statistical Computing
Platform: x86_64-pc-linux-gnu (64-bit)
R is free software and comes with ABSOLUTELY NO WARRANTY.
You are welcome to redistribute it under certain conditions.
Type 'license()' or 'licence()' for distribution details.
R is a collaborative project with many contributors.
Type 'contributors()' for more information and
'citation()' on how to cite R or R packages in publications.
Type 'demo()' for some demos, 'help()' for on-line help, or
'help.start()' for an HTML browser interface to help.
Type 'q()' to quit R.
> library(SimComp)
> png(filename="/home/ddbj/snapshot/RGM3/R_CC/result/SimComp/plot.SimCi.Rd_%03d_medium.png", width=480, height=480)
> ### Name: plot.SimCi
> ### Title: Plot function for SimCi-objects
> ### Aliases: plot.SimCi
> ### Keywords: print
>
> ### ** Examples
>
> # Example 1:
> # Simultaneous confidence intervals related to a comparison of the groups
> # B and H against the standard S, on endpoint Thromb.count, assuming unequal
> # variances for the groups. This is an extension of the well-known Dunnett-
> # intervals to the case of heteroscedasticity.
>
> data(coagulation)
>
> interv1 <- SimCiDiff(data=coagulation, grp="Group", resp="Thromb.count",
+ type="Dunnett", base=3, alternative="greater", covar.equal=FALSE)
> interv1
Simultaneous 95% confidence intervals for differences of means of multiple endpoints
Assumption: Heterogeneous covariance matrices for the groups
comparison endpoint estimate lower.raw upper.raw lower upper
1 B - S Thromb.count 0.1217 -0.0367 Inf -0.0681 Inf
2 H - S Thromb.count 0.0435 -0.1344 Inf -0.1695 Inf
> plot(interv1)
>
> # Example 2:
> # Simultaneous confidence intervals related to a comparisons of the groups
> # B and H against the standard S, simultaneously on all endpoints, assuming
> # unequal covariance matrices for the groups. This is an extension of the well-
> # known Dunnett-intervals to the case of heteroscedasticity and multiple
> # endpoints.
>
> data(coagulation)
>
> interv2 <- SimCiDiff(data=coagulation, grp="Group", resp=c("Thromb.count","ADP","TRAP"),
+ type="Dunnett", base=3, alternative="greater", covar.equal=FALSE)
> summary(interv2)
Contrast matrix:
Multiple Comparisons of Means: Dunnett Contrasts
B H S
B - S 1 0 -1
H - S 0 1 -1
Estimated covariance matrices of the data:
$B
Thromb.count ADP TRAP
Thromb.count 0.0626 0.0565 -0.0102
ADP 0.0565 0.0638 0.0054
TRAP -0.0102 0.0054 0.0963
$H
Thromb.count ADP TRAP
Thromb.count 0.0943 0.0637 0.0663
ADP 0.0637 0.0518 0.0446
TRAP 0.0663 0.0446 0.1157
$S
Thromb.count ADP TRAP
Thromb.count 0.0318 0.0132 0.0598
ADP 0.0132 0.0079 0.0269
TRAP 0.0598 0.0269 0.1376
Estimated correlation matrices of the data:
$B
Thromb.count ADP TRAP
Thromb.count 1.0000 0.8937 -0.1314
ADP 0.8937 1.0000 0.0687
TRAP -0.1314 0.0687 1.0000
$H
Thromb.count ADP TRAP
Thromb.count 1.0000 0.9121 0.6348
ADP 0.9121 1.0000 0.5770
TRAP 0.6348 0.5770 1.0000
$S
Thromb.count ADP TRAP
Thromb.count 1.0000 0.8338 0.9033
ADP 0.8338 1.0000 0.8161
TRAP 0.9033 0.8161 1.0000
Estimated correlation matrix of the comparisons:
[,1] [,2] [,3] [,4] [,5] [,6]
[1,] 1.0000 0.8494 0.3122 0.2833 0.1708 0.3755
[2,] 0.8494 1.0000 0.2387 0.1335 0.1158 0.1917
[3,] 0.3122 0.2387 1.0000 0.3417 0.2232 0.5550
[4,] 0.2833 0.1335 0.3417 1.0000 0.8869 0.7054
[5,] 0.1708 0.1158 0.2232 0.8869 1.0000 0.5818
[6,] 0.3755 0.1917 0.5550 0.7054 0.5818 1.0000
comparison endpoint estimate lower.raw upper.raw lower upper
1 B - S Thromb.count 0.1217 -0.0367 Inf -0.1119 Inf
2 B - S ADP 0.2121 0.0691 Inf 0.0067 Inf
3 B - S TRAP 0.1053 -0.1395 Inf -0.2582 Inf
4 H - S Thromb.count 0.0435 -0.1344 Inf -0.2139 Inf
5 H - S ADP 0.0842 -0.0398 Inf -0.0928 Inf
6 H - S TRAP 0.0711 -0.1784 Inf -0.2941 Inf
> par(mfrow=c(1,3)); plot(interv2)
>
> # Example 3:
> # Simultaneous confidence intervals for ratios of means, related to an all-pair
> # comparison of the groups B, H and S, simultaneously on all endpoints, assuming unequal
> # covariance matrices for the groups. This is an extension of the well-known Tukey-
> # intervals to the case of heteroscedasticity and multiple endpoints, and in terms of
> # ratios of means instead of differences.
>
> data(coagulation)
>
> interv3 <- SimCiRat(data=coagulation, grp="Group", resp=c("Thromb.count","ADP","TRAP"),
+ type="Tukey", alternative="two.sided", covar.equal=FALSE)
> summary(interv3)
Numerator contrast matrix:
B H S
H/B 0 1 0
S/B 0 0 1
S/H 0 0 1
Denominator contrast matrix:
B H S
H/B 1 0 0
S/B 1 0 0
S/H 0 1 0
Estimated covariance matrices of the data:
$B
Thromb.count ADP TRAP
Thromb.count 0.0626 0.0565 -0.0102
ADP 0.0565 0.0638 0.0054
TRAP -0.0102 0.0054 0.0963
$H
Thromb.count ADP TRAP
Thromb.count 0.0943 0.0637 0.0663
ADP 0.0637 0.0518 0.0446
TRAP 0.0663 0.0446 0.1157
$S
Thromb.count ADP TRAP
Thromb.count 0.0318 0.0132 0.0598
ADP 0.0132 0.0079 0.0269
TRAP 0.0598 0.0269 0.1376
Estimated correlation matrices of the data:
$B
Thromb.count ADP TRAP
Thromb.count 1.0000 0.8937 -0.1314
ADP 0.8937 1.0000 0.0687
TRAP -0.1314 0.0687 1.0000
$H
Thromb.count ADP TRAP
Thromb.count 1.0000 0.9121 0.6348
ADP 0.9121 1.0000 0.5770
TRAP 0.6348 0.5770 1.0000
$S
Thromb.count ADP TRAP
Thromb.count 1.0000 0.8338 0.9033
ADP 0.8338 1.0000 0.8161
TRAP 0.9033 0.8161 1.0000
Estimated correlation matrix of the comparisons:
[,1] [,2] [,3] [,4] [,5] [,6] [,7] [,8] [,9]
[1,] 1.0000 0.8966 0.3141 0.4869 0.5076 -0.0492 -0.6719 -0.6593 -0.3202
[2,] 0.8966 1.0000 0.3320 0.5023 0.6555 0.0297 -0.5467 -0.6448 -0.2596
[3,] 0.3141 0.3320 1.0000 -0.0699 0.0426 0.4093 -0.4001 -0.3912 -0.4731
[4,] 0.4869 0.5023 -0.0699 1.0000 0.8494 0.3781 0.3198 0.2025 0.4258
[5,] 0.5076 0.6555 0.0426 0.8494 1.0000 0.2918 0.1697 0.1545 0.2448
[6,] -0.0492 0.0297 0.4093 0.3781 0.2918 1.0000 0.3740 0.2566 0.6102
[7,] -0.6719 -0.5467 -0.4001 0.3198 0.1697 0.3740 1.0000 0.8869 0.7084
[8,] -0.6593 -0.6448 -0.3912 0.2025 0.1545 0.2566 0.8869 1.0000 0.5874
[9,] -0.3202 -0.2596 -0.4731 0.4258 0.2448 0.6102 0.7084 0.5874 1.0000
comparison endpoint estimate lower.raw upper.raw lower upper
1 H/B Thromb.count 0.9213 0.7046 1.1853 0.6276 1.309
2 H/B ADP 0.8746 0.7020 1.0905 0.6415 1.194
3 H/B TRAP 0.9589 0.6675 1.3617 0.5682 1.580
4 S/B Thromb.count 0.8775 0.7201 1.0803 0.6577 1.197
5 S/B ADP 0.7921 0.6714 0.9547 0.6294 1.042
6 S/B TRAP 0.8733 0.5726 1.2760 0.4497 1.532
7 S/H Thromb.count 0.9525 0.7584 1.2287 0.6888 1.395
8 S/H ADP 0.9056 0.7701 1.0868 0.7227 1.183
9 S/H TRAP 0.9107 0.5928 1.3566 0.4683 1.647
> par(mfrow=c(3,1)); plot(interv3)
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> dev.off()
null device
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Created & Maintained by Osamu Ogasawara (osamu.ogasawara@gmail.com) and